Asynchronous distributed optimization has been studied at least since
the seminal work of Tsitsiklis et al. [1]}. In
recent years, asynchronous schemes have played a key role in solving
large-scale machine learning and online optimization problems. For
instance, Google's distbelif [2]} was one
of the earliest implementation of asynchronous distributed optimization
for large-scale training in machine learning. In general, the literature
pertaining to asynchronous distributed optimization and online learning
focuses on two aspects: algorithmic development and convergence analysis.
